Imaging devices are routinely used in the medical industry. Examples of such imaging devices include, but are not limited to, Positron Emission Tomography (PET) imaging devices and Single Photon Emission Computed Tomography (SPECT) imaging devices. The imaging devices capture two dimensional projection images of a target or a patient. The two dimensional projection images are reconstructed into a three dimensional volumetric image of the target or the patient. The reconstructed images are often corrupted by uniform attenuation, non-uniform attenuation, and shift variant blurring.
One prior art method for compensating for the shift variant blurring involves the use of a frequency-distance principle (FDP) algorithm. The FIR algorithm is a pre-processing algorithm. In other words, the FDP algorithm attempts to compensate for shift variant blurring in the two dimensional projection images of the target prior to using the two dimensional projection images to reconstruct the three dimensional image of the target. The FDP algorithm makes an assumption that there is no uniform or non-uniform attenuation present in the two dimensional projection images generated by the imaging devices. This assumption often results in the amplification of noise and the introduction of artifacts in the reconstructed three dimensional image of the target.
Another prior art method for compensating for shift variant blurring involves the use of an intrinsic iterative reconstruction algorithm. The intrinsic iterative reconstruction algorithm is implemented during the reconstruction of the two dimensional projection images of the target into a three dimensional image of the target. While the intrinsic iterative reconstruction algorithm is effective at compensating for shift variant blurring, the implementation of the intrinsic iterative reconstruction algorithm can take hours or even days. Such long processing times to obtain a three dimensional image of a target can lead to delays in diagnosis and treatment of a patient's medical condition.
Another prior art method involves the use of shift invariant filters to attempt to compensate for shift variant blurring. Shift invariant filters are typically not very effective at filtering shift variant blurring.
Thus what is needed is a system and method for deblurring data corrupted by shift variant blurring to overcome one or more of the challenges and/or obstacles described above.